Performance detection method

ABSTRACT

Proposed is a performance detection method for use in detection of performance characteristics of air conditioning equipment. The method includes, for the standard operational status for the air conditioning equipment, capturing a plurality of standard operational parameters to generate performance models; and, during the actual operational status, capturing a plurality of actual operational parameters for conducting analysis based on performance models so as to determine the performance characteristics of the air conditioning equipment and send out a warning when the performance characteristics of the air conditioning equipment are determined to be abnormal. The invention improves on the detection accuracy of prior techniques and enables effective control of air conditioning equipment that eventually saves electrical power and costs as a result.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to a performance detectionmethod, and more particularly, to a method for detecting the performancecharacteristics of air conditioning equipment with high accuracy.

2. Description of Related Art

In recent years, electrical power consumption and demand are greatlyincreasing. However, electrical power generation can easily lead toenvironmental pollution, for example, carbon dioxide emissions causingglobal warming, so it is difficult to acquire land for buildingelectrical power plants. The development of electrical power resourcesand the construction of power transmission and distribution systemscannot meet the demand for ever-growing electrical power consumption.Thus, electrical power supply is often insufficient, and measures aretaken to limit electrical power consumption during the peak-demandperiods, including higher energy rates during peak periods. As such,ways need to be found and applied to efficiently reduce electrical powerconsumption to avoid electrical power supply shortages and high energycosts.

According to statistics, air conditioning equipment consumes more powerthan most other kinds of electrical equipment. For example, in asemiconductor manufacturing fab, there is processing equipment, testequipment and/or air conditioning equipment. Generally, the airconditioning equipment (constant temperature water baths, airconditioning systems and chillers) account for roughly 27% of the totalelectrical power consumption of such a semiconductor manufacturing fab.Accordingly, improving the efficiency of utilization of the airconditioning helps to reduce electrical power consumption.

In order to improve the efficiency of air conditioning equipment, suchair conditioning equipment should be selected according to theenvironment in which the air conditioning equipment will be used.Further, the performance characteristics of the air conditioningequipment should be timely and accurately detected such that the use ofthe air conditioning equipment can be adjusted or timely repairedaccording to the performance characteristics, thereby reducingelectrical power consumption or preventing failure of the airconditioning equipment. Generally, the current performance detectionmethod detects the performance characteristics of the air conditioningequipment based on operational parameters (such as part load ratio)provided by manufacturers and the experience of the maintenance staff.

However, air conditioning equipment is used in variable environmentswhile the operational parameters provided by the manufacturers can onlybe used as a reference for a specific environment. In addition, theoperational characteristics of the air conditioning equipment changewith such factors as design construction, operational time, maintenancecondition, chiller efficiency, or variation of peripheral equipment. Ofcourse, the system operation of the air conditioning equipment alsochanges with such factors as climate, temperature, humidity and seasonalchanges.

Therefore, the current detection method has low accuracy and cannottimely detect the operational circumstances of air conditioningequipment such that the use of the air conditioning equipment can betimely adjusted or maintenance can be flexibly and optimally arranged.

Therefore, there exists a strong need in the art for a performancedetection method that can timely and accurately detect the performancecharacteristics of air conditioning equipment such that the use of theair conditioning equipment can be flexibly adjusted and/or maintenancefor the air conditioning equipment can be arranged, thereby reducingequipment downtime, energy costs and electrical power consumption.

SUMMARY OF THE INVENTION

Accordingly, the present invention provides a performance detectionmethod for detecting performance characteristics of air conditioningequipment according to actual operational parameters captured duringactual operational status of the air conditioning equipment, the methodcomprising the steps of: (1) capturing standard operational parametersfor the standard operational status of the air conditioning equipment;(2) generating performance models according to the standard operationalparameters of the air conditioning equipment; (3) analyzing the actualoperational parameters according to the performance models so as todetermine the performance characteristics of the air conditioningequipment; and (4) sending out a warning if the performancecharacteristics of the air conditioning equipment are determined to beabnormal, or, otherwise, capturing the actual operational parametersduring the actual operational status of the air conditioning equipmentand returning to step (3).

In a preferred embodiment, the air conditioning equipment comprises anair conditioner, an air handling unit, a heat pump, a water coolingtower, a central air conditioning system and/or a chiller. The standardoperational parameters and actual operational parameters are power rate,average power consumption, energy efficiency rate, coefficient ofperformance, part load ratio (PLR), electrical power consumption perrefrigeration ton, cooling water inlet and outlet temperatures, coolingwater flow, ice water inlet and outlet temperatures, ice water flow,refrigerant pressure and/or atmospheric humidity and temperature forboth the standard operation status and actual operation status of theair conditioning equipment.

In another preferred embodiment, the performance models can be trenddiagrams and/or curve diagrams. In addition, the actual operationalparameters can be analyzed by fall point analysis, regression analysisand/or correlation analysis.

Compared with the prior art, the present invention improves thedetection accuracy. Through the present invention, the performancecharacteristics of the air conditioning equipment can be timely andefficiently detected such that maintenance staff can flexibly adjust theuse of the air conditioning equipment and/or arrange maintenance for theair conditioning equipment, thereby reducing the downtime of the airconditioning equipment and also reducing costs and electrical powerconsumption.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows the structure of a chiller;

FIG. 2 is a flow diagram showing a performance detection methodaccording to the present invention;

FIG. 3 is a diagram of a performance model of the power rate and thepart load ratio (PLR);

FIG. 4 is a diagram of a performance model of the coefficient ofperformance (COP) and part load ratio (PLR);

FIG. 5 is a diagram showing fall point analysis based on the performancemodel utilizing the power rate and part load ration (PLR); and

FIG. 6 is a diagram showing fall point analysis based on the performancemodel utilizing the coefficient of performance (COP) and part load ratio(PLR).

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following illustrative embodiments are provided to illustrate thedisclosure of the present invention, wherein these and other advantagesand effects will be apparent to those skilled in the art after readingthe disclosure of this specification.

Common air conditioning equipment is mainly divided into a materialoutput system and a material cooling system. Through heat exchangebetween the material output system and the material cooling system, asuitable material such as ice water or chilled air is continuouslyoutput. The air conditioning equipment may be such as an airconditioner, a central air conditioning system, or a chiller.

FIG. 1 shows a chiller 1, which comprises a material output system 10and a material cooling system 11.

As shown in the drawing, through heat exchange between the materialoutput system 10 and the material cooling system 11 of the chiller 1,warm water is chilled to cool water that can further be used insubsequent processes or facilities (the cool water can be, for example,used for cooling). Operational parameters such as atmospherictemperature, atmospheric relative humidity, refrigerant quality, thetemperature of the warm water, refrigerant inlet temperature,refrigerant outlet temperature, and the temperature of the cool water,can be measured through related measurement/sensing devices.

FIG. 2 is a flow diagram showing a performance detection method appliedto the air conditioning equipment according to the present invention.

First, in step S21, a plurality of standard operational parameters iscaptured and stored for the standard daily operational status of the airconditioning equipment. The air conditioning equipment can be airconditioner, a central air conditioning system, and/or a chiller, likethe chiller shown in FIG. 1. The standard operational parameters can bethe power rate, average power consumption, energy efficiency rate,coefficient of performance (COP), part load ratio (PLR), electricalpower consumption per refrigeration ton, cooling water inlet and outlettemperatures, cooling water flow, ice water inlet and outlettemperature, ice water flow, refrigerant pressure and/or atmospherichumidity and temperature for the standard operation status of the airconditioning equipment. In a preferred embodiment, step S21 can beperformed periodically or continuously to capture the standardoperational parameters of the air conditioning equipment through relatedparameter capturing devices (not shown). Further, the captured standardoperational parameters can be stored. In addition, some descriptiveinformation can be set in step S21. The descriptive information cancomprise abnormal circumstances, causes of the abnormal circumstances,measures of correction, and/or maintenance actions. The descriptiveinformation can further be stored, for example, as a form in a database.Then, the process goes to step S22.

In step S22, performance models such as those shown in FIGS. 3 and 4 aregenerated according to the standard operational parameters of the airconditioning equipment in normal operation. In particular, at least twoof the standard operational parameters can be generalized to setup aperformance model (for example, a mathematic model or a trend function)of the air conditioning equipment in normal operation. Preferably, theperformance models can be presented as trend diagrams or curve diagramsand the performance models can further be stored. Then, the process goesto step S23.

In step S23, during the actual operation of the air conditioningequipment, a plurality of actual operational parameters of the airconditioning equipment is captured. The actual operational parameterscan be power rate, average power consumption, energy efficiency rate,coefficient of performance (COP), part load ratio (PLR), electricalpower consumption per refrigeration ton, cooling water inlet and outlettemperatures, cooling water flow, ice water inlet and outlettemperatures, ice water flow, refrigerant pressure and/or atmospherichumidity and temperature in the actual operation status of the airconditioning equipment. Preferably, step S23 is periodically performedup to continuously or randomly performed so as to capture the actualoperational parameters in the actual operation status of the airconditioning equipment through related parameter capturing devices.Further, the captured actual operational parameters can be stored. Then,the process goes to step S24.

In step S24, the captured actual operational parameters are analyzedaccording to the performance models so as to determine whether theperformance characteristics and/or operational circumstances of the airconditioning equipment are abnormal, if the performance characteristicsand/or operational circumstances of the air conditioning equipment aredetermined to be abnormal, the process goes to step S25, and otherwise,the process returns to step S23.

According to a preferred embodiment of the present invention, thecaptured actual operational parameters can be analyzed through fallpoint analysis. The fall point analysis comprises single value fallpoint analysis and inter-value correlation fall point analysis. Forexample, the single value fall point analysis can be used to determinewhether the actual operational parameters such as atmospherictemperature, atmospheric relative humidity, ice water inlet temperature,ice water outlet temperature, refrigerant inlet temperature andrefrigerant outlet temperature are higher or lower compared with thestandard operational parameters, wherein corresponding weight values maybe determined or applied. Alternately, the inter-value correlation fallpoint analysis can be used to determine, for example, whether thecorrelations between parameters such as the atmospheric temperature andthe refrigerant outlet temperature are higher or lower compared with thecorrelation between the atmospheric temperature and the refrigerantoutlet temperature in previous similar circumstances, wherein thecorresponding weight values may also be determined or applied. Tables 1and 2 show result examples by using the above-described single valuefall point analysis and inter-value correlation fall point analysis.Subsequently, whether the performance characteristics are abnormal canbe determined and causes of the abnormal circumstances can be analyzedthrough artificial intelligence software, a genetic algorithm and/or aneural network. It should be noted that after the causes of the abnormalcircumstances are determined, the causes can be introduced into arelated database (not shown) for future equipment diagnosis and databasediagnosis so as to prevent blunders.

TABLE 1 (Single Value Fall Point Analysis) Atmospheric AtmosphericRefrigerant Ice Water Inlet Temperature Relative Humidity QualityTemperature Normal Normal Normal Normal (weight 0) (weight 0) (weight 0)(weight 0) Refrigerant Inlet Refrigerant Outlet Ice Water OutletTemperature Temperature Temperature Higher Higher Normal (weight + 1)(weight + 1) (weight 0)

TABLE 2 (Inter-Value Correlation Fall Point Analysis) Refrigerant InletAtmospheric Temperature Relative Humidity Higher (weight + 1) Normal(weight 0)

In another preferred embodiment, step S24 can use regression analysis,correlation analysis and/or tree analysis to analyze the captured actualoperational parameters, and use artificial intelligence software,genetic algorithms and/or neural networks to determine whether theperformance characteristics and/or operational circumstances of the airconditioning equipment are abnormal and the causes of the abnormalcircumstances, or predict/detect abnormal circumstances that may occurso as to avoid them. The causes of the abnormal circumstances can beintroduced to the related database so as to facilitate future equipmentdiagnosis.

In step S25, if the performance characteristics of the air conditioningequipment and/or operational circumstances are determined to beabnormal, a warning signal or message is sent out. For example, if theperformance characteristics or operational circumstances of the airconditioning equipment are determined to be worse, a warning is sentout. When the warning is sent out, preferably the cause of the abnormalcircumstance is analyzed and corresponding measures are providedaccording to the above-described description information. In particular,the possible cause can be automatically discerned using a neuralalgorithm and/or genetic algorithm, thereby providing recommendedmeasures for providing troubleshooting and/or maintenance to facilitatediagnosis, repair and maintenance by maintenance staff, as shown inTable 3.

TABLE 3 Recommended Possible Abnormal Circumstances Possible CausesTroubleshooting/Maintenance low operational efficiency (water coolingtype) pipe is (water cooling type) clean filter and blocked or waterflows too slow; repair or replace floating ball; examine (air coolingtype) dirty heat water level; clean condenser; (air cooling sink orfailure of cooling fans type) clean or replace heat sink hightemperature of refrigerant too high atmospheric wet bulb, increase watertower, examine fan fan failure or poor ventilation and clear air inletand outlet of cooling tower of water tower. high temperature of icewater improper operation of chiller examine whether chiller fails bigdifference in refrigerant too small flow of refrigerant Examine whetherfilter valve is blocked, temperature and, if it is, clean the filtervalve; examine whether the valve can be normally opened; clean waterscale. Insufficient volume of refrigerant high pressure is too high oradd refrigerant. low pressure is too low

FIG. 3 illustrates a performance model (mathematical model) setup bygeneralizing the standard operational parameters (power rate and partload ratio) that are captured periodically/continuously for the standardoperational status of the air conditioning equipment by relatedparameter capturing devices. As shown in the drawing, the performancemodel is a performance trend diagram with a trend function ofy=0.167x²+0.7659x+0.0222, wherein R²=0.8756 represents the correlationcoefficient between the y-axis (power rate) and the x-axis (part loadratio). Generally, the higher the correlation coefficient, the higher isthe correlation between the y-axis (power rate) and the x-axis (partload ratio). A performance model with a correlation coefficient that islarger than 0.75 is a preferred reference value.

FIG. 4 illustrates a performance model setup by generalizing thestandard operational parameters (coefficient of performance and partload ratio) that are captured continuously or periodically in thestandard operational status of the air conditioning equipment by relatedparameter capturing devices. As shown in the drawing, the performancemodel is a performance trend diagram, wherein, if the part load ratio isset to be 72%, the operation efficiency of the air conditioningequipment is optimal (coefficient of performance=1.775). Based on this,maintenance staff can flexibly adjust the settings for the airconditioning equipment.

FIG. 5 is a diagram showing fall point analysis of the actualoperational parameters based on the performance model of FIG. 3.Referring to step S24 of FIG. 2 and to FIG. 5, the captured actualoperational parameters (measured values) fall below the trend line ofthe performance model, which generally means the performancecharacteristics and operational circumstances of the air conditioningequipment are much better. Thus, through such as neural algorithm and/orgenetic algorithm, it can be determined that abnormal circumstances mayoccur to the performance characteristics or operational circumstances ofthe air conditioning equipment. Otherwise, if the actual operationalparameters (measured values) fall above the trend line, it means thatthe performance characteristics and operational circumstances of the airconditioning equipment are worse. The correlation between the actualoperational parameters and standard operational parameters can also beanalyzed through regression analysis and/or correlation analysis.

FIG. 6 is a diagram showing fall point analysis of the actualoperational parameters based on the performance model of FIG. 4.Referring to step S24 of FIG. 2 and to FIG. 6, the actual operationalparameters (measured values) do not fall in the optimal region shown inFIG. 6, which means that the performance characteristics or operationalcircumstances of the air conditioning equipment may be abnormal. Thecorrelation between the actual operational parameters and standardoperational parameters can also be analyzed through regression analysisand/or correlation analysis. It should be noted that the peak region ofthe curve is the optimum region where the performance characteristics ofthe air conditioning equipment are optimal.

Therefore, the performance detection method of the present invention hasthe following advantages:

-   -   (1) High accuracy. The detection accuracy is improved through        automatic and standard detection procedures.    -   (2) Pre-warning. By determining whether the performance        characteristics or operational circumstances of the air        conditioning equipment are abnormal or whether a more serious        abnormal circumstance will likely occur, the usage of the air        conditioning equipment can be adjusted and maintenance can be        arranged so as to save electrical power consumption and prevent        failure of the air conditioning equipment.

(3) Rapid process. The method allows maintenance staff to rapidlyrecognize possible causes and the method automatically provides at leasta preferred measure.

-   -   Compared with the prior art, the present invention improves the        detection accuracy. In addition, through the present invention,        the performance characteristics and operational circumstances of        the air conditioning equipment can be timely, accurately and        efficiently detected such that maintenance staff can flexibly        adjust the usage/settings of the air conditioning equipment        and/or arrange maintenance for the air conditioning equipment,        thereby reducing the failure probability of the air conditioning        equipment and reducing costs and electrical power consumption.

The above-described descriptions of the detailed embodiments areprovided to illustrate the preferred implementation according to thepresent invention, and are not intended to limit the scope of thepresent invention. Many modifications and variations completed by thosewith ordinary skill in the art can be made and should be considered tofall within the scope of the invention as defined by the appendedclaims.

1. A performance detection method for detecting performancecharacteristics of air conditioning equipment according to actualoperational parameters captured during actual operational status of theair conditioning equipment, comprising the steps of: (1) capturingstandard operational parameters during standard operational status ofthe air conditioning equipment; (2) generating performance modelsaccording to the standard operational parameters of the air conditioningequipment; (3) analyzing the actual operational parameters according tothe performance models so as to determine the performancecharacteristics of the air conditioning equipment; and (4) sending out awarning if the performance characteristics of the air conditioningequipment are determined to be abnormal, and otherwise, if theperformance characteristics of the air conditioning equipment aredetermined to be normal, capturing the actual operational parametersduring the actual operational status of the air conditioning equipmentand returning to step (3).
 2. The method of claim 1, further comprisingthe step of setting description information, wherein the descriptioninformation comprises abnormal circumstances, causes of the abnormalcircumstances, and recommended measures for providing troubleshootingand/or maintenance.
 3. The method of claim 2, when the performancecharacteristics of the air conditioning equipment are determined to beabnormal, further comprising the step of obtaining the descriptioninformation corresponding to the abnormal circumstance so as to providecorresponding corrective measures.
 4. The method of claim 1, furthercomprising the step of storing the standard operational parameters andperformance models of the air conditioning equipment according to thestandard operational parameters of the air conditioning equipment. 5.The method of claim 4, wherein the standard operational parameters arepower rate, average power consumption, energy efficiency rate,coefficient of performance, part load ratio, electrical powerconsumption per refrigeration ton, cooling water inlet and outlettemperatures, cooling water flow, ice water inlet and outlettemperatures, ice water flow, refrigerant pressure and/or atmospherichumidity and temperature for the standard operational status of the airconditioning equipment.
 6. The method of claim 4, wherein theperformance models are trend diagrams and/or curve diagrams.
 7. Themethod of claim 1, wherein the standard operational parameters andactual operational parameters are periodically and/or continuouslycaptured.
 8. The method of claim 7, wherein the actual operationalparameters are power rate, average power consumption, energy efficiencyrate, coefficient of performance, part load ratio, electrical powerconsumption per refrigeration ton, cooling water inlet and outlettemperatures, cooling water flow, ice water inlet and outlettemperatures, ice water flow, refrigerant pressure and/or atmospherichumidity and temperature for the actual operation status of the airconditioning equipment.
 9. The method of claim 7, wherein the standardoperational parameters are power rate, average power consumption, energyefficiency rate, coefficient of performance, part load ratio, electricalpower consumption per refrigeration ton, cooling water inlet and outlettemperatures, cooling water flow, ice water inlet and outlettemperatures, ice water flow, refrigerant pressure and/or atmospherichumidity and temperature for the standard operational status of the airconditioning equipment.
 10. The method of claim 1, wherein the actualoperational parameters are analyzed by fall point analysis, regressionanalysis and/or correlation analysis.
 11. The method of claim 10,wherein the actual operational parameters are power rate, average powerconsumption, energy efficiency rate, coefficient of performance, partload ratio, electrical power consumption per refrigeration ton, coolingwater inlet and outlet temperatures, cooling water flow, ice water inletand outlet temperatures, ice water flow, refrigerant pressure and/oratmospheric humidity and temperature for the actual operation status ofthe air conditioning equipment.
 12. The method of claim 1, wherein theair conditioning equipment is an air conditioner, an air handling unit,a heat pump, a cooling water tower, a central air conditioning systemand/or a chiller.